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A/B Testing with Fat Tails

Author

Listed:
  • Eduardo M. Azevedo
  • Alex Deng
  • José Luis Montiel Olea
  • Justin Rao
  • E. Glen Weyl

Abstract

We propose a new framework for optimal experimentation, which we term the “A/B testing problem.” Our model departs from the existing literature by allowing for fat tails. Our key insight is that the optimal strategy depends on whether most gains accrue from typical innovations or from rare, unpredictable large successes. If the tails of the unobserved distribution of innovation quality are not too fat, the standard approach of using a few high-powered “big” experiments is optimal. However, if the distribution is very fat tailed, a “lean” strategy of trying more ideas, each with possibly smaller sample sizes, is preferred. Our theoretical results, along with an empirical analysis of Microsoft Bing’s EXP platform, suggest that simple changes to business practices could increase innovation productivity.

Suggested Citation

  • Eduardo M. Azevedo & Alex Deng & José Luis Montiel Olea & Justin Rao & E. Glen Weyl, 2020. "A/B Testing with Fat Tails," Journal of Political Economy, University of Chicago Press, vol. 128(12), pages 4614-4000.
  • Handle: RePEc:ucp:jpolec:doi:10.1086/710607
    DOI: 10.1086/710607
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    Cited by:

    1. Drugov, Mikhail & Ryvkin, Dmitry, 2020. "Tournament rewards and heavy tails," Journal of Economic Theory, Elsevier, vol. 190(C).
    2. Dominic Coey & Kenneth Hung, 2022. "Empirical Bayes Selection for Value Maximization," Papers 2210.03905, arXiv.org, revised Jan 2023.
    3. Azevedo, Eduardo M. & Mao, David & Montiel Olea, José Luis & Velez, Amilcar, 2023. "The A/B testing problem with Gaussian priors," Journal of Economic Theory, Elsevier, vol. 210(C).
    4. Dirk Bergemann & Yang Cai & Grigoris Velegkas & Mingfei Zhao, 2022. "Is Selling Complete Information (Approximately) Optimal?," Cowles Foundation Discussion Papers 2324, Cowles Foundation for Research in Economics, Yale University.
    5. Todd A. Hall & Sharique Hasan, 2022. "Organizational decision-making and the returns to experimentation," Journal of Organization Design, Springer;Organizational Design Community, vol. 11(4), pages 129-144, December.
    6. Curello, Gregorio, 2023. "Incentives for Collective Innovation," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277708, Verein für Socialpolitik / German Economic Association.
    7. Ron Berman & Christophe Van den Bulte, 2022. "False Discovery in A/B Testing," Management Science, INFORMS, vol. 68(9), pages 6762-6782, September.
    8. Annie Liang & Xiaosheng Mu & Vasilis Syrgkanis, 2021. "Dynamically Aggregating Diverse Information," Working Papers 2021-43, Princeton University. Economics Department..

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